BigBite解析，Tesla FSD就是一个端到端大模型

Core Viewpoint - Tesla's Full Self-Driving (FSD) is fundamentally a large model that utilizes a significant neural network architecture to achieve end-to-end driving capabilities, contrary to claims that it relies on numerous smaller models for various tasks [7][17]. Summary by Sections FSD Model Architecture - Tesla FSD is characterized as a large model, confirmed by Ashok at ICCV, which utilizes a massive neural network for computations from Photon In to Control Out [7][14]. - The architecture includes numerous model parameter files, which are primarily small task heads rather than independent models, indicating a more complex integration than previously assumed [6][10]. Parameter File Insights - The discovery of hundreds of neural network parameter files has led to skepticism about FSD being a large model; however, these files are largely associated with smaller tasks rather than the core end-to-end model [8][10]. - The parameter sizes for HW3 and HW4 show significant growth, with HW4's B core reaching 7.5GB, indicating a substantial increase in model complexity and capability [8][12]. Memory and Bandwidth Considerations - HW3's limited memory bandwidth of 68GB/s restricts the model size to approximately 1.8 billion parameters, while HW4's bandwidth of 384GB/s allows for a theoretical capacity of around 10 billion parameters [12][13]. - The use of a mixture of experts (MOE) architecture enables Tesla to optimize memory usage and enhance model performance without exceeding bandwidth limitations [13][16]. Technological Advancements - The assertion that Tesla's technology is outdated is challenged by the argument that significant engineering innovations contribute to advancements, similar to the development of reusable rockets [17]. - The integration of advanced engineering practices and innovative architectures positions Tesla as a leader in the autonomous driving sector, countering claims of technological inferiority [17].